21 to 30 of 70 Results
Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 908.2 MB -
MD5: 5c153c9c868e0f53820dda95dda3c9a9
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Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 446.9 MB -
MD5: be1356d1aa354aeb1040d8bca4b50146
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Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 921.9 MB -
MD5: 987fae4feb0a5443487e7c9f73e52f57
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Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 621.9 MB -
MD5: e743e498584c1652cf6cc9b4e098bdba
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Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 459.6 MB -
MD5: 051b766e496ce3fba067a3b21b4683ae
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Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 1.0 GB -
MD5: 1764e40e690ca17eed4dd654018ad252
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Jun 30, 2025 -
Seabird Tracking Data: Red-Footed Booby Acceleration Data
Comma Separated Values - 894.6 MB -
MD5: 805ea0a123547bd9d82bc2fd54821a5c
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Jun 22, 2025
Dunn, Ruth E.; Freeman, Robin M.; Nicoll, Malcolm A.; Trevail, Alice M.; Votier, Stephen C., 2025, "Seabird Tracking Data: Red-Footed Booby Bird-Bourne Video Data", https://doi.org/10.7910/DVN/U0BGFJ, Harvard Dataverse, V2
Footage recorded via bird-borne video cameras on the backs of red-footed booby (Sula sula) adults during the breeding season, while undertaking central-place foraging trips. Data collected using DVL400M Little Leonardos from two individuals at Nelson’s Island (5.68° S, 72.32° E) in the Chagos Archipelago in January 2024. Cameras were programmed to... |
ZIP Archive - 13.1 GB -
MD5: f33471d62428eb0be5117731bae7b611
Two sub-folders for the two individuals, each containing 7-10 videos that are approximately 30 minutes in length. |
Jun 21, 2025
Freeman, Robin; Dunn, Ruth; Nicoll, Malcolm; Trevail, Alice; Votier, Stephen; Wood, Hannah, 2025, "Data for "Deep neural networks to predict foraging behaviour: salt-water immersion data can accurately predict diving in seabirds"", https://doi.org/10.7910/DVN/GHWM5U, Harvard Dataverse, V1
Dataset for the paper "Deep neural networks to predict foraging behaviour: salt-water immersion data can accurately predict diving in seabirds" by Swaby et al. This dataset was collected from Axytrek GPS/dive/accelerometer loggers on the backs of adult red-footed booby (Sula sula) during the central place foraging from Barton Point, Diego Garcia (-... |